{"title":"一种具有层次先验的鲁棒测向估计方法","authors":"Q. Wu, D. Fuhrmann","doi":"10.1109/MDSP.1989.97078","DOIUrl":null,"url":null,"abstract":"Summary form only given. An estimator that can maintain acceptable performance for the hard conditions on which the maximum-likelihood estimator fails, i.e. when the signal-to-noise ratio falls below a certain threshold, is derived by introducing the prior information into the estimation. The prior information may be the approximate signal powers and the noise power. Because the available prior information is always vague, a robust way to incorporate it is developed. Simulation results showing the significant performance improvement are given.<<ETX>>","PeriodicalId":340681,"journal":{"name":"Sixth Multidimensional Signal Processing Workshop,","volume":"6 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1989-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A robust estimator for direction finding with hierarchical prior\",\"authors\":\"Q. Wu, D. Fuhrmann\",\"doi\":\"10.1109/MDSP.1989.97078\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Summary form only given. An estimator that can maintain acceptable performance for the hard conditions on which the maximum-likelihood estimator fails, i.e. when the signal-to-noise ratio falls below a certain threshold, is derived by introducing the prior information into the estimation. The prior information may be the approximate signal powers and the noise power. Because the available prior information is always vague, a robust way to incorporate it is developed. Simulation results showing the significant performance improvement are given.<<ETX>>\",\"PeriodicalId\":340681,\"journal\":{\"name\":\"Sixth Multidimensional Signal Processing Workshop,\",\"volume\":\"6 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1989-09-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Sixth Multidimensional Signal Processing Workshop,\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/MDSP.1989.97078\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Sixth Multidimensional Signal Processing Workshop,","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/MDSP.1989.97078","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A robust estimator for direction finding with hierarchical prior
Summary form only given. An estimator that can maintain acceptable performance for the hard conditions on which the maximum-likelihood estimator fails, i.e. when the signal-to-noise ratio falls below a certain threshold, is derived by introducing the prior information into the estimation. The prior information may be the approximate signal powers and the noise power. Because the available prior information is always vague, a robust way to incorporate it is developed. Simulation results showing the significant performance improvement are given.<>